Member of Technical Staff, AI Engineering (India)

Overview

Build data systems that improve the quality, scale, and safety of data generated and acquired for frontier model training, spanning synthetic data generation and data anonymization/PII removal. Partner with researchers, domain experts, legal and compliance stakeholders, and customers to turn ambiguous data questions into experiments, pipelines, and durable products.

What you'll do

• Design and build systems that improve the quality, scale, and safety of data for frontier model training, spanning synthetic data generation and data anonymization/PII removal.
• Translate ambiguous research, partner, or compliance needs into clear hypotheses, experiments, evaluation plans, and production-quality implementations.
• Build and improve data-processing pipelines, evaluation frameworks, benchmarks, and quality-control systems.
• Run fast, rigorous iteration loops by prototyping, evaluating, interpreting results, and turning learnings into the next system or product.
• Partner with researchers, domain experts, and, where relevant, legal and compliance teams to ensure data is both high-utility and responsibly handled.
• Identify repeatable patterns across engagements and productize them into reusable software and platforms.
• Raise the technical bar through strong design judgment, clear communication, code quality, and mentorship.

What you'll need

• 2–10 years of recent, demonstrated experience in one or more of: synthetic/LLM-generated data, post-training and model-evaluation work, privacy engineering, or data anonymization/de-identification at scale.
• A hands-on individual contributor track record; this is not a team-lead or engineering-management role.
• Strong Python skills.
• Ability to write clean, efficient, scalable software for large, messy, real-world datasets.
• Sound judgment for reasoning about data quality, risk, and utility, including forming hypotheses, choosing meaningful metrics, diagnosing failures, and distinguishing signal from noise.
• Experience designing systems, including tradeoffs around quality, scale, reliability, and reuse.
• Comfort operating in an ambiguous, fast-moving environment with substantial ownership.
• Collaborative, low-ego communication and the ability to work effectively with researchers, engineers, domain experts, and customers.

Nice to have

• Experience building or operating large-scale synthetic or LLM-generated data pipelines for model training.
• Experience building or operating large-scale data de-identification or anonymization systems, ideally involving relational or graph-structured data, with experience preserving referential/relationship integrity after anonymization.
• Experience developing LLM/agent benchmarks, evaluation methodologies, annotation systems, or data-quality frameworks.
• Research or applied work on reinforcement learning, alignment, model behavior, synthetic data, or human-in-the-loop systems.
• Prior work in a regulated or high-sensitivity data environment, including healthcare, finance, HR/people data, or government.
• Experience with re-identification risk assessment and privacy auditing.
• Published research, meaningful open-source contributions, or evidence of technical leadership in ML systems, data engineering, or AI research.
• Experience productizing research or repeated customer work into robust, reusable platforms.

Details

• Location: Bangalore, India.